Survival World: Exploring Alternative Patient Futures Through Trajectory Reasoning for Survival Prediction

Longitudinal survival prediction requires reasoning about how a patient's conditionmay evolve beyond the observed history. We introduce SURVIVAL WORLD (SW),an empirical world model that makes these possible futures explicit and traceable.The model organizes observed training transitions into an executable graph and usesa language model to compile feature semantics into a bounded retrieval program. Ateach rollout state, the program adapts retrieval to the current measurements, trends,and observation context, then advances to an actual training history. A sharedsurvival head evaluates the resulting trajectories, and averaging their survivalprobabilities integrates alternative futures into a unified prediction. Programsexecute locally, with reusable computations and no patient records sent to thelanguage model. In an exploratory evaluation on PBCseq and a MIMIC-IV cohortat the second admission, with adaptive selection using test scores, the methodachieves the highest selected concordance among seven baseline families and threeworld model controls. Component ablations identify the roles of empirical retrieval,dynamic weighting, and path aggregation. Beyond prediction, exploring alternativenode sequences reveals interpretable state profiles associated with different modeledsurvival trajectories, enabling traceable hypothesis generation. Survival World connects semantic program design with empirical patient dynamics to support both survival prediction and exploration of possible futures.